Stochastic material modelling and multi-fidelity uncertainty quantification of macroscopic bone tissue

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource, 189 Seiten

Language: Englisch

Notes: Braunschweig, Technische Universität Braunschweig, Dissertation, 2022

Keyword: Finite-Elemente-Methode
Computersimulation
Mathematisches Modell
Stochastisches Modell
Biomechanik

Event: Veröffentlichung

(where): Braunschweig

(who): Technische Universität Braunschweig

(when): 2022

Creator: Shivanand, Sharana Kumar

Contributor: Matthies, Hermann G.
Rosić, Bojana

URN: urn:nbn:de:gbv:084-2022121610451

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:26 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Shivanand, Sharana Kumar
Matthies, Hermann G.
Rosić, Bojana
Technische Universität Braunschweig

Time of origin

2022

Other Objects (12)

An Application of Multi-Fidelity Uncertainty Quantification for Computational Wind Engineering

Hochschulschrift

Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach

Multi-fidelity User Interface Specifications

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Multi‐Fidelity Optimization using Reduced Order Models

Graph Laplacian-based spectral multi-fidelity modeling

Multi-fidelity Surrogate Modelling of Wall Mounted Cubes

Multi-fidelity Modeling and Optimization of Biogas Plants

Multi-fidelity Bayesian machine learning for global optimization

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

A modular multi-fidelity scheme combining data-driven reduced order models for structural analysis under uncertainty

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change

An Application of Multi-Fidelity Uncertainty Quantification for Computational Wind Engineering

Hochschulschrift

Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach

Multi-fidelity User Interface Specifications

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Multi‐Fidelity Optimization using Reduced Order Models

Graph Laplacian-based spectral multi-fidelity modeling

Multi-fidelity Surrogate Modelling of Wall Mounted Cubes

Multi-fidelity Modeling and Optimization of Biogas Plants

Multi-fidelity Bayesian machine learning for global optimization

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

A modular multi-fidelity scheme combining data-driven reduced order models for structural analysis under uncertainty

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change

An Application of Multi-Fidelity Uncertainty Quantification for Computational Wind Engineering

Hochschulschrift

Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach

Multi-fidelity User Interface Specifications

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Multi‐Fidelity Optimization using Reduced Order Models

Graph Laplacian-based spectral multi-fidelity modeling

Multi-fidelity Surrogate Modelling of Wall Mounted Cubes

Multi-fidelity Modeling and Optimization of Biogas Plants

Multi-fidelity Bayesian machine learning for global optimization

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

A modular multi-fidelity scheme combining data-driven reduced order models for structural analysis under uncertainty

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Stochastic material modelling and multi-fidelity uncertainty quantification of macroscopic bone tissue

Object Details

Classification and Topics

Contributors, Places and Time

Further information

Data provider

Associated

Time of origin

Other Objects (12)

An Application of Multi-Fidelity Uncertainty Quantification for Computational Wind Engineering

Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach

Multi-fidelity User Interface Specifications

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Multi‐Fidelity Optimization using Reduced Order Models

Graph Laplacian-based spectral multi-fidelity modeling

Multi-fidelity Surrogate Modelling of Wall Mounted Cubes

Multi-fidelity Modeling and Optimization of Biogas Plants

Multi-fidelity Bayesian machine learning for global optimization

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

A modular multi-fidelity scheme combining data-driven reduced order models for structural analysis under uncertainty

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change

An Application of Multi-Fidelity Uncertainty Quantification for Computational Wind Engineering

Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach

Multi-fidelity User Interface Specifications

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Multi‐Fidelity Optimization using Reduced Order Models

Graph Laplacian-based spectral multi-fidelity modeling

Multi-fidelity Surrogate Modelling of Wall Mounted Cubes

Multi-fidelity Modeling and Optimization of Biogas Plants

Multi-fidelity Bayesian machine learning for global optimization

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

A modular multi-fidelity scheme combining data-driven reduced order models for structural analysis under uncertainty

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change

An Application of Multi-Fidelity Uncertainty Quantification for Computational Wind Engineering

Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach

Multi-fidelity User Interface Specifications

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

Multi‐Fidelity Optimization using Reduced Order Models

Graph Laplacian-based spectral multi-fidelity modeling

Multi-fidelity Surrogate Modelling of Wall Mounted Cubes

Multi-fidelity Modeling and Optimization of Biogas Plants

Multi-fidelity Bayesian machine learning for global optimization

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

A modular multi-fidelity scheme combining data-driven reduced order models for structural analysis under uncertainty

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change

Related objects

Reset password